Asked about A complete tabular project, end to end
Walk me through how you would approach a new tabular classification problem.
An answer that works
Look at the data and target balance, hold out a test set, get a dummy baseline, build a preprocessing pipeline so cross-validation is leak-free, try a regularised linear model then gradient boosting, choose on cross-validation, evaluate once on the test set, then analyse errors by slice and check calibration if probabilities are used.
Then they push further
Why do gradient boosted trees usually beat neural networks on tables?
Tabular features are heterogeneous, often with sharp thresholds and irregular interactions, which trees capture naturally. Trees need no scaling, handle missing values and categories well, and are strong with modest data. Neural networks shine when there is structure like images or text to exploit.
How would you explain a prediction to a stakeholder?
Show the main features driving the model overall with permutation importance, and for a single case, local explanations such as SHAP values, with the caveat that they explain the model's behaviour, not the real-world cause.
What gets marked up
Strong answers mention a baseline, a pipeline to avoid leakage, a single use of the test set, and slice-level error analysis.
Reading an answer is not being able to give one. The section that teaches this is A complete tabular project, end to end, 6 hours of Supervised learning.
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